Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1000
Missing cells661
Missing cells (%)3.1%
Duplicate rows3
Duplicate rows (%)0.3%
Total size in memory164.2 KiB
Average record size in memory168.1 B

Variable types

Text1
Numeric9
Categorical9
DateTime1
Boolean1

Alerts

Dataset has 3 (0.3%) duplicate rowsDuplicates
10k Running Time Prediction is highly overall correlated with Age of customer and 1 other fieldsHigh correlation
Age of customer is highly overall correlated with 10k Running Time Prediction and 1 other fieldsHigh correlation
Biking Hours per Week is highly overall correlated with Calories Burned per Week and 1 other fieldsHigh correlation
Calories Burned per Week is highly overall correlated with Biking Hours per Week and 2 other fieldsHigh correlation
Ctry is highly overall correlated with TownHigh correlation
Running Hours per Week is highly overall correlated with Calories Burned per Week and 1 other fieldsHigh correlation
Total Training Hours per Week is highly overall correlated with Biking Hours per Week and 2 other fieldsHigh correlation
Town is highly overall correlated with CtryHigh correlation
VO2 Max is highly overall correlated with 10k Running Time Prediction and 1 other fieldsHigh correlation
ID has 22 (2.2%) missing values Missing
Age of customer has 22 (2.2%) missing values Missing
Sex has 22 (2.2%) missing values Missing
Ctry has 22 (2.2%) missing values Missing
Town has 61 (6.1%) missing values Missing
Swimming Hours per Week has 22 (2.2%) missing values Missing
Biking Hours per Week has 46 (4.6%) missing values Missing
Running Hours per Week has 22 (2.2%) missing values Missing
Total Training Hours per Week has 22 (2.2%) missing values Missing
VO2 Max has 25 (2.5%) missing values Missing
10k Running Time Prediction has 22 (2.2%) missing values Missing
Calories Burned per Week has 41 (4.1%) missing values Missing
Support Cases of Customer has 22 (2.2%) missing values Missing
Customer Years has 22 (2.2%) missing values Missing
Most current software update has 23 (2.3%) missing values Missing
Goal of Training has 50 (5.0%) missing values Missing
Preferred Training Daytime has 46 (4.6%) missing values Missing
Subscription Type has 23 (2.3%) missing values Missing
Color of Watch has 54 (5.4%) missing values Missing
Synchronisation has 50 (5.0%) missing values Missing
User of latest model has 22 (2.2%) missing values Missing
Swimming Hours per Week has 69 (6.9%) zeros Zeros
Biking Hours per Week has 29 (2.9%) zeros Zeros
Running Hours per Week has 41 (4.1%) zeros Zeros
Customer Years has 149 (14.9%) zeros Zeros

Reproduction

Analysis started2025-01-26 12:27:26.442403
Analysis finished2025-01-26 12:27:44.069823
Duration17.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID
Text

Missing 

Distinct971
Distinct (%)99.3%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
2025-01-26T13:27:44.478974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4890
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique964 ?
Unique (%)98.6%

Sample

1st rowC0001
2nd rowC0003
3rd rowC0004
4th rowC0005
5th rowC0006
ValueCountFrequency (%)
c0059 2
 
0.2%
c0942 2
 
0.2%
c0708 2
 
0.2%
c0641 2
 
0.2%
c0640 2
 
0.2%
c0568 2
 
0.2%
c0482 2
 
0.2%
c1000 1
 
0.1%
c0010 1
 
0.1%
c0011 1
 
0.1%
Other values (961) 961
98.3%
2025-01-26T13:27:44.948476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1268
25.9%
C 978
20.0%
6 300
 
6.1%
5 296
 
6.1%
8 296
 
6.1%
9 295
 
6.0%
2 295
 
6.0%
3 293
 
6.0%
1 292
 
6.0%
4 291
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1268
25.9%
C 978
20.0%
6 300
 
6.1%
5 296
 
6.1%
8 296
 
6.1%
9 295
 
6.0%
2 295
 
6.0%
3 293
 
6.0%
1 292
 
6.0%
4 291
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1268
25.9%
C 978
20.0%
6 300
 
6.1%
5 296
 
6.1%
8 296
 
6.1%
9 295
 
6.0%
2 295
 
6.0%
3 293
 
6.0%
1 292
 
6.0%
4 291
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1268
25.9%
C 978
20.0%
6 300
 
6.1%
5 296
 
6.1%
8 296
 
6.1%
9 295
 
6.0%
2 295
 
6.0%
3 293
 
6.0%
1 292
 
6.0%
4 291
 
6.0%

Age of customer
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)4.9%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean40.529652
Minimum-1
Maximum64
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)1.0%
Memory size7.9 KiB
2025-01-26T13:27:45.090468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile19
Q129
median41
Q353
95-th percentile62
Maximum64
Range65
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.07218
Coefficient of variation (CV)0.34720702
Kurtosis-0.70657215
Mean40.529652
Median Absolute Deviation (MAD)12
Skewness-0.20584005
Sum39638
Variance198.02624
MonotonicityNot monotonic
2025-01-26T13:27:45.256857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
19 32
 
3.2%
43 32
 
3.2%
56 30
 
3.0%
22 29
 
2.9%
33 28
 
2.8%
35 26
 
2.6%
30 25
 
2.5%
52 25
 
2.5%
46 24
 
2.4%
20 23
 
2.3%
Other values (38) 704
70.4%
ValueCountFrequency (%)
-1 10
 
1.0%
18 16
1.6%
19 32
3.2%
20 23
2.3%
21 11
 
1.1%
22 29
2.9%
23 20
2.0%
24 17
1.7%
25 16
1.6%
26 20
2.0%
ValueCountFrequency (%)
64 15
1.5%
63 20
2.0%
62 23
2.3%
61 16
1.6%
60 19
1.9%
59 22
2.2%
58 21
2.1%
57 15
1.5%
56 30
3.0%
55 23
2.3%

Sex
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
Male
345 
Female
320 
Other
313 

Length

Max length6
Median length5
Mean length4.9744376
Min length4

Characters and Unicode

Total characters4865
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 345
34.5%
Female 320
32.0%
Other 313
31.3%
(Missing) 22
 
2.2%

Length

2025-01-26T13:27:45.412765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:45.675269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 345
35.3%
female 320
32.7%
other 313
32.0%

Most occurring characters

ValueCountFrequency (%)
e 1298
26.7%
a 665
13.7%
l 665
13.7%
M 345
 
7.1%
F 320
 
6.6%
m 320
 
6.6%
O 313
 
6.4%
t 313
 
6.4%
h 313
 
6.4%
r 313
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1298
26.7%
a 665
13.7%
l 665
13.7%
M 345
 
7.1%
F 320
 
6.6%
m 320
 
6.6%
O 313
 
6.4%
t 313
 
6.4%
h 313
 
6.4%
r 313
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1298
26.7%
a 665
13.7%
l 665
13.7%
M 345
 
7.1%
F 320
 
6.6%
m 320
 
6.6%
O 313
 
6.4%
t 313
 
6.4%
h 313
 
6.4%
r 313
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1298
26.7%
a 665
13.7%
l 665
13.7%
M 345
 
7.1%
F 320
 
6.6%
m 320
 
6.6%
O 313
 
6.4%
t 313
 
6.4%
h 313
 
6.4%
r 313
 
6.4%

Ctry
Categorical

High correlation  Missing 

Distinct7
Distinct (%)0.7%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
Australia
200 
USA
199 
UK
191 
India
186 
Germany
179 
Other values (2)
23 

Length

Max length11
Median length7
Mean length5.2546012
Min length2

Characters and Unicode

Total characters5139
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUK
2nd rowAustralia
3rd rowIndia
4th rowGermany
5th rowIndia

Common Values

ValueCountFrequency (%)
Australia 200
20.0%
USA 199
19.9%
UK 191
19.1%
India 186
18.6%
Germany 179
17.9%
Germayn 19
 
1.9%
UnknownLand 4
 
0.4%
(Missing) 22
 
2.2%

Length

2025-01-26T13:27:45.844712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:45.974056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
australia 200
20.4%
usa 199
20.3%
uk 191
19.5%
india 186
19.0%
germany 179
18.3%
germayn 19
 
1.9%
unknownland 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 788
15.3%
n 400
 
7.8%
A 399
 
7.8%
r 398
 
7.7%
U 394
 
7.7%
i 386
 
7.5%
s 200
 
3.9%
t 200
 
3.9%
u 200
 
3.9%
l 200
 
3.9%
Other values (12) 1574
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 788
15.3%
n 400
 
7.8%
A 399
 
7.8%
r 398
 
7.7%
U 394
 
7.7%
i 386
 
7.5%
s 200
 
3.9%
t 200
 
3.9%
u 200
 
3.9%
l 200
 
3.9%
Other values (12) 1574
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 788
15.3%
n 400
 
7.8%
A 399
 
7.8%
r 398
 
7.7%
U 394
 
7.7%
i 386
 
7.5%
s 200
 
3.9%
t 200
 
3.9%
u 200
 
3.9%
l 200
 
3.9%
Other values (12) 1574
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 788
15.3%
n 400
 
7.8%
A 399
 
7.8%
r 398
 
7.7%
U 394
 
7.7%
i 386
 
7.5%
s 200
 
3.9%
t 200
 
3.9%
u 200
 
3.9%
l 200
 
3.9%
Other values (12) 1574
30.6%

Town
Categorical

High correlation  Missing 

Distinct15
Distinct (%)1.6%
Missing61
Missing (%)6.1%
Memory size7.9 KiB
Berlin
76 
New York
72 
Melbourne
70 
Birmingham
70 
Chicago
64 
Other values (10)
587 

Length

Max length11
Median length9
Mean length7.6304579
Min length5

Characters and Unicode

Total characters7165
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham
2nd rowSydney
3rd rowBangalore
4th rowMunich
5th rowMumbai

Common Values

ValueCountFrequency (%)
Berlin 76
 
7.6%
New York 72
 
7.2%
Melbourne 70
 
7.0%
Birmingham 70
 
7.0%
Chicago 64
 
6.4%
Bangalore 64
 
6.4%
Brisbane 63
 
6.3%
Sydney 60
 
6.0%
Munich 60
 
6.0%
Mumbai 59
 
5.9%
Other values (5) 281
28.1%
(Missing) 61
 
6.1%

Length

2025-01-26T13:27:46.139349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
berlin 76
 
7.1%
new 72
 
6.7%
york 72
 
6.7%
melbourne 70
 
6.6%
birmingham 70
 
6.6%
chicago 64
 
6.0%
bangalore 64
 
6.0%
brisbane 63
 
5.9%
sydney 60
 
5.6%
munich 60
 
5.6%
Other values (7) 396
37.1%

Most occurring characters

ValueCountFrequency (%)
e 759
 
10.6%
n 681
 
9.5%
r 532
 
7.4%
i 518
 
7.2%
a 501
 
7.0%
o 430
 
6.0%
l 322
 
4.5%
g 313
 
4.4%
h 308
 
4.3%
B 273
 
3.8%
Other values (20) 2528
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 759
 
10.6%
n 681
 
9.5%
r 532
 
7.4%
i 518
 
7.2%
a 501
 
7.0%
o 430
 
6.0%
l 322
 
4.5%
g 313
 
4.4%
h 308
 
4.3%
B 273
 
3.8%
Other values (20) 2528
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 759
 
10.6%
n 681
 
9.5%
r 532
 
7.4%
i 518
 
7.2%
a 501
 
7.0%
o 430
 
6.0%
l 322
 
4.5%
g 313
 
4.4%
h 308
 
4.3%
B 273
 
3.8%
Other values (20) 2528
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 759
 
10.6%
n 681
 
9.5%
r 532
 
7.4%
i 518
 
7.2%
a 501
 
7.0%
o 430
 
6.0%
l 322
 
4.5%
g 313
 
4.4%
h 308
 
4.3%
B 273
 
3.8%
Other values (20) 2528
35.3%

Swimming Hours per Week
Real number (ℝ)

Missing  Zeros 

Distinct440
Distinct (%)45.0%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.2261963
Minimum0
Maximum9.97
Zeros69
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:46.303913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.02
median2.02
Q33.1875
95-th percentile5.2015
Maximum9.97
Range9.97
Interquartile range (IQR)2.1675

Descriptive statistics

Standard deviation1.5952543
Coefficient of variation (CV)0.71658294
Kurtosis0.69797561
Mean2.2261963
Median Absolute Deviation (MAD)1.085
Skewness0.79058962
Sum2177.22
Variance2.5448363
MonotonicityNot monotonic
2025-01-26T13:27:46.750515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69
 
6.9%
1.56 7
 
0.7%
1.37 6
 
0.6%
1.03 5
 
0.5%
3.64 5
 
0.5%
2.36 5
 
0.5%
1.4 5
 
0.5%
3.1 5
 
0.5%
3.26 5
 
0.5%
2.2 5
 
0.5%
Other values (430) 861
86.1%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
0 69
6.9%
0.01 1
 
0.1%
0.04 1
 
0.1%
0.06 2
 
0.2%
0.07 1
 
0.1%
0.08 2
 
0.2%
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.12 1
 
0.1%
ValueCountFrequency (%)
9.97 1
0.1%
8.54 1
0.1%
7.92 1
0.1%
7.61 1
0.1%
7.58 1
0.1%
6.98 1
0.1%
6.92 2
0.2%
6.69 1
0.1%
6.67 1
0.1%
6.59 1
0.1%

Biking Hours per Week
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct649
Distinct (%)68.0%
Missing46
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean5.529109
Minimum0
Maximum16.11
Zeros29
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:46.896993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.556
Q12.7675
median5.11
Q37.87
95-th percentile11.668
Maximum16.11
Range16.11
Interquartile range (IQR)5.1025

Descriptive statistics

Standard deviation3.4993719
Coefficient of variation (CV)0.63289978
Kurtosis-0.27214715
Mean5.529109
Median Absolute Deviation (MAD)2.49
Skewness0.5261631
Sum5274.77
Variance12.245604
MonotonicityNot monotonic
2025-01-26T13:27:47.068192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
2.9%
4.56 6
 
0.6%
5.3 4
 
0.4%
8.31 4
 
0.4%
4.16 4
 
0.4%
1.44 4
 
0.4%
7.11 4
 
0.4%
1.66 4
 
0.4%
3.52 4
 
0.4%
16.11 3
 
0.3%
Other values (639) 888
88.8%
(Missing) 46
 
4.6%
ValueCountFrequency (%)
0 29
2.9%
0.05 1
 
0.1%
0.09 1
 
0.1%
0.1 2
 
0.2%
0.11 1
 
0.1%
0.13 1
 
0.1%
0.25 1
 
0.1%
0.28 1
 
0.1%
0.29 1
 
0.1%
0.3 1
 
0.1%
ValueCountFrequency (%)
16.11 3
0.3%
16.02 1
 
0.1%
15.67 1
 
0.1%
15.39 1
 
0.1%
15.34 1
 
0.1%
15.17 1
 
0.1%
15.03 1
 
0.1%
14.7 1
 
0.1%
14.69 1
 
0.1%
14.64 1
 
0.1%

Running Hours per Week
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct542
Distinct (%)55.4%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean3.2622188
Minimum0
Maximum11.87
Zeros41
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:47.230129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q11.635
median2.93
Q34.6
95-th percentile7.25
Maximum11.87
Range11.87
Interquartile range (IQR)2.965

Descriptive statistics

Standard deviation2.1657088
Coefficient of variation (CV)0.66387601
Kurtosis0.25569535
Mean3.2622188
Median Absolute Deviation (MAD)1.49
Skewness0.69799854
Sum3190.45
Variance4.6902947
MonotonicityNot monotonic
2025-01-26T13:27:47.410864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
4.1%
2.98 6
 
0.6%
1.15 6
 
0.6%
1.81 6
 
0.6%
2.16 5
 
0.5%
2.37 5
 
0.5%
2.66 5
 
0.5%
4.57 5
 
0.5%
2.19 5
 
0.5%
2.01 4
 
0.4%
Other values (532) 890
89.0%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
0 41
4.1%
0.02 1
 
0.1%
0.05 2
 
0.2%
0.07 2
 
0.2%
0.1 1
 
0.1%
0.11 3
 
0.3%
0.12 1
 
0.1%
0.14 1
 
0.1%
0.15 2
 
0.2%
0.16 2
 
0.2%
ValueCountFrequency (%)
11.87 1
0.1%
11.42 1
0.1%
10.58 1
0.1%
10.15 1
0.1%
9.88 1
0.1%
9.45 1
0.1%
9.44 1
0.1%
9.3 1
0.1%
9.16 1
0.1%
9.12 1
0.1%

Total Training Hours per Week
Real number (ℝ)

High correlation  Missing 

Distinct732
Distinct (%)74.8%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean11.446155
Minimum0.15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:47.567280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile4.06
Q17.38
median10.22
Q314.0975
95-th percentile20.6955
Maximum100
Range99.85
Interquartile range (IQR)6.7175

Descriptive statistics

Standard deviation8.0385306
Coefficient of variation (CV)0.70229088
Kurtosis73.226449
Mean11.446155
Median Absolute Deviation (MAD)3.275
Skewness6.92802
Sum11194.34
Variance64.617974
MonotonicityNot monotonic
2025-01-26T13:27:47.729314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6 5
 
0.5%
9.8 5
 
0.5%
100 5
 
0.5%
8.72 5
 
0.5%
19.28 4
 
0.4%
5.46 4
 
0.4%
9.44 4
 
0.4%
5.08 4
 
0.4%
13.44 4
 
0.4%
9.61 4
 
0.4%
Other values (722) 934
93.4%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
0.15 1
0.1%
0.45 1
0.1%
1.26 2
0.2%
1.29 1
0.1%
1.66 1
0.1%
1.67 1
0.1%
1.77 1
0.1%
1.98 1
0.1%
2.08 1
0.1%
2.24 1
0.1%
ValueCountFrequency (%)
100 5
0.5%
26.36 1
 
0.1%
26.21 1
 
0.1%
25.66 1
 
0.1%
25.5 1
 
0.1%
25.44 1
 
0.1%
24.98 1
 
0.1%
24.95 1
 
0.1%
24.77 1
 
0.1%
23.96 1
 
0.1%

VO2 Max
Real number (ℝ)

High correlation  Missing 

Distinct858
Distinct (%)88.0%
Missing25
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.374066
Minimum10.22
Maximum247.2578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:48.067748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.22
5-th percentile22.521
Q142.07
median53.4
Q360.095
95-th percentile67.372
Maximum247.2578
Range237.0378
Interquartile range (IQR)18.025

Descriptive statistics

Standard deviation21.459113
Coefficient of variation (CV)0.41770322
Kurtosis47.733242
Mean51.374066
Median Absolute Deviation (MAD)8.03
Skewness5.1949147
Sum50089.715
Variance460.49353
MonotonicityNot monotonic
2025-01-26T13:27:48.230885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247.2578 7
 
0.7%
57.68 4
 
0.4%
54.56 3
 
0.3%
56.15 3
 
0.3%
57.29 3
 
0.3%
58.78 3
 
0.3%
29.83 3
 
0.3%
52.24 3
 
0.3%
52.57 3
 
0.3%
60.4 3
 
0.3%
Other values (848) 940
94.0%
(Missing) 25
 
2.5%
ValueCountFrequency (%)
10.22 1
0.1%
10.49 1
0.1%
10.55 1
0.1%
12.6 1
0.1%
12.73 1
0.1%
13.91 1
0.1%
13.96 1
0.1%
14.46 1
0.1%
14.47 1
0.1%
14.52 1
0.1%
ValueCountFrequency (%)
247.2578 7
0.7%
80.54 1
 
0.1%
75.07 1
 
0.1%
74.3 1
 
0.1%
74.12 1
 
0.1%
73.74 1
 
0.1%
72.61 1
 
0.1%
72.54 1
 
0.1%
72.03 1
 
0.1%
71.83 1
 
0.1%

10k Running Time Prediction
Real number (ℝ)

High correlation  Missing 

Distinct805
Distinct (%)82.3%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean45.610521
Minimum29.12
Maximum67.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:48.380276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29.12
5-th percentile35.262
Q140.3025
median43.885
Q350.2575
95-th percentile60.0175
Maximum67.06
Range37.94
Interquartile range (IQR)9.955

Descriptive statistics

Standard deviation7.549771
Coefficient of variation (CV)0.16552696
Kurtosis-0.10391685
Mean45.610521
Median Absolute Deviation (MAD)4.42
Skewness0.68258758
Sum44607.09
Variance56.999042
MonotonicityNot monotonic
2025-01-26T13:27:48.540374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.93 4
 
0.4%
41.14 4
 
0.4%
41.72 4
 
0.4%
41.74 4
 
0.4%
39.69 4
 
0.4%
45.54 3
 
0.3%
41.38 3
 
0.3%
44.12 3
 
0.3%
43.35 3
 
0.3%
41.8 3
 
0.3%
Other values (795) 943
94.3%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
29.12 1
0.1%
30.51 1
0.1%
30.87 1
0.1%
30.9 1
0.1%
31.23 1
0.1%
31.81 1
0.1%
32.25 1
0.1%
32.6 1
0.1%
32.64 1
0.1%
32.89 1
0.1%
ValueCountFrequency (%)
67.06 1
0.1%
66.96 1
0.1%
66.66 1
0.1%
66.27 1
0.1%
66.26 1
0.1%
66.21 1
0.1%
66.19 1
0.1%
66.16 1
0.1%
65.75 1
0.1%
65.67 1
0.1%

Calories Burned per Week
Real number (ℝ)

High correlation  Missing 

Distinct948
Distinct (%)98.9%
Missing41
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean5487.5348
Minimum-47.26
Maximum13280.06
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size7.9 KiB
2025-01-26T13:27:48.695361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-47.26
5-th percentile1957.574
Q13661.94
median5168.41
Q37000.84
95-th percentile10152.875
Maximum13280.06
Range13327.32
Interquartile range (IQR)3338.9

Descriptive statistics

Standard deviation2499.7416
Coefficient of variation (CV)0.45553089
Kurtosis-0.026214563
Mean5487.5348
Median Absolute Deviation (MAD)1660.19
Skewness0.53369794
Sum5262545.9
Variance6248708.3
MonotonicityNot monotonic
2025-01-26T13:27:48.858969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5
 
0.5%
1688.93 2
 
0.2%
4460.39 2
 
0.2%
4785.35 2
 
0.2%
3233.52 2
 
0.2%
6919.41 2
 
0.2%
7664.95 2
 
0.2%
3694.24 2
 
0.2%
4692.4 1
 
0.1%
4286.93 1
 
0.1%
Other values (938) 938
93.8%
(Missing) 41
 
4.1%
ValueCountFrequency (%)
-47.26 1
 
0.1%
-28.25 1
 
0.1%
100 5
0.5%
534.26 1
 
0.1%
747.94 1
 
0.1%
762.5 1
 
0.1%
765.89 1
 
0.1%
846.38 1
 
0.1%
880.5 1
 
0.1%
969.23 1
 
0.1%
ValueCountFrequency (%)
13280.06 1
0.1%
13033.62 1
0.1%
12616.9 1
0.1%
12568.14 1
0.1%
12544.94 1
0.1%
12429.64 1
0.1%
12324.68 1
0.1%
12280.77 1
0.1%
12106.35 1
0.1%
11905.24 1
0.1%

Support Cases of Customer
Categorical

Missing 

Distinct5
Distinct (%)0.5%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
1.0
208 
0.0
202 
3.0
201 
4.0
184 
2.0
183 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2934
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 208
20.8%
0.0 202
20.2%
3.0 201
20.1%
4.0 184
18.4%
2.0 183
18.3%
(Missing) 22
 
2.2%

Length

2025-01-26T13:27:48.989892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:49.086078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 208
21.3%
0.0 202
20.7%
3.0 201
20.6%
4.0 184
18.8%
2.0 183
18.7%

Most occurring characters

ValueCountFrequency (%)
0 1180
40.2%
. 978
33.3%
1 208
 
7.1%
3 201
 
6.9%
4 184
 
6.3%
2 183
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1180
40.2%
. 978
33.3%
1 208
 
7.1%
3 201
 
6.9%
4 184
 
6.3%
2 183
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1180
40.2%
. 978
33.3%
1 208
 
7.1%
3 201
 
6.9%
4 184
 
6.3%
2 183
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1180
40.2%
. 978
33.3%
1 208
 
7.1%
3 201
 
6.9%
4 184
 
6.3%
2 183
 
6.2%

Customer Years
Real number (ℝ)

Missing  Zeros 

Distinct12
Distinct (%)1.2%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean3.7924335
Minimum0
Maximum11
Zeros149
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T13:27:49.192932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9165928
Coefficient of variation (CV)0.76905576
Kurtosis-1.109428
Mean3.7924335
Median Absolute Deviation (MAD)2
Skewness0.3405941
Sum3709
Variance8.5065138
MonotonicityNot monotonic
2025-01-26T13:27:49.509823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 149
14.9%
1 140
14.0%
3 110
11.0%
2 106
10.6%
8 92
9.2%
4 91
9.1%
5 82
8.2%
6 77
7.7%
7 66
6.6%
9 60
6.0%
Other values (2) 5
 
0.5%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
0 149
14.9%
1 140
14.0%
2 106
10.6%
3 110
11.0%
4 91
9.1%
5 82
8.2%
6 77
7.7%
7 66
6.6%
8 92
9.2%
9 60
6.0%
ValueCountFrequency (%)
11 3
 
0.3%
10 2
 
0.2%
9 60
6.0%
8 92
9.2%
7 66
6.6%
6 77
7.7%
5 82
8.2%
4 91
9.1%
3 110
11.0%
2 106
10.6%
Distinct393
Distinct (%)40.2%
Missing23
Missing (%)2.3%
Memory size7.9 KiB
Minimum2023-10-11 00:00:00
Maximum2025-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-26T13:27:49.657454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:49.826301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Goal of Training
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing50
Missing (%)5.0%
Memory size7.9 KiB
Fitness
332 
Recreation
327 
Competition
291 

Length

Max length11
Median length10
Mean length9.2578947
Min length7

Characters and Unicode

Total characters8795
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFitness
2nd rowFitness
3rd rowCompetition
4th rowRecreation
5th rowRecreation

Common Values

ValueCountFrequency (%)
Fitness 332
33.2%
Recreation 327
32.7%
Competition 291
29.1%
(Missing) 50
 
5.0%

Length

2025-01-26T13:27:49.984793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:50.076493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fitness 332
34.9%
recreation 327
34.4%
competition 291
30.6%

Most occurring characters

ValueCountFrequency (%)
e 1277
14.5%
i 1241
14.1%
t 1241
14.1%
n 950
10.8%
o 909
10.3%
s 664
7.5%
F 332
 
3.8%
R 327
 
3.7%
r 327
 
3.7%
c 327
 
3.7%
Other values (4) 1200
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1277
14.5%
i 1241
14.1%
t 1241
14.1%
n 950
10.8%
o 909
10.3%
s 664
7.5%
F 332
 
3.8%
R 327
 
3.7%
r 327
 
3.7%
c 327
 
3.7%
Other values (4) 1200
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1277
14.5%
i 1241
14.1%
t 1241
14.1%
n 950
10.8%
o 909
10.3%
s 664
7.5%
F 332
 
3.8%
R 327
 
3.7%
r 327
 
3.7%
c 327
 
3.7%
Other values (4) 1200
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1277
14.5%
i 1241
14.1%
t 1241
14.1%
n 950
10.8%
o 909
10.3%
s 664
7.5%
F 332
 
3.8%
R 327
 
3.7%
r 327
 
3.7%
c 327
 
3.7%
Other values (4) 1200
13.6%

Preferred Training Daytime
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing46
Missing (%)4.6%
Memory size7.9 KiB
Morning
334 
Evening
321 
Afternoon
299 

Length

Max length9
Median length7
Mean length7.6268344
Min length7

Characters and Unicode

Total characters7276
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowEvening
3rd rowAfternoon
4th rowEvening
5th rowMorning

Common Values

ValueCountFrequency (%)
Morning 334
33.4%
Evening 321
32.1%
Afternoon 299
29.9%
(Missing) 46
 
4.6%

Length

2025-01-26T13:27:50.196873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:50.290259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
morning 334
35.0%
evening 321
33.6%
afternoon 299
31.3%

Most occurring characters

ValueCountFrequency (%)
n 1908
26.2%
o 932
12.8%
g 655
 
9.0%
i 655
 
9.0%
r 633
 
8.7%
e 620
 
8.5%
M 334
 
4.6%
E 321
 
4.4%
v 321
 
4.4%
A 299
 
4.1%
Other values (2) 598
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1908
26.2%
o 932
12.8%
g 655
 
9.0%
i 655
 
9.0%
r 633
 
8.7%
e 620
 
8.5%
M 334
 
4.6%
E 321
 
4.4%
v 321
 
4.4%
A 299
 
4.1%
Other values (2) 598
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1908
26.2%
o 932
12.8%
g 655
 
9.0%
i 655
 
9.0%
r 633
 
8.7%
e 620
 
8.5%
M 334
 
4.6%
E 321
 
4.4%
v 321
 
4.4%
A 299
 
4.1%
Other values (2) 598
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1908
26.2%
o 932
12.8%
g 655
 
9.0%
i 655
 
9.0%
r 633
 
8.7%
e 620
 
8.5%
M 334
 
4.6%
E 321
 
4.4%
v 321
 
4.4%
A 299
 
4.1%
Other values (2) 598
 
8.2%

Subscription Type
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing23
Missing (%)2.3%
Memory size7.9 KiB
Free
341 
Premium
334 
Basic
302 

Length

Max length7
Median length5
Mean length5.3346981
Min length4

Characters and Unicode

Total characters5212
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree
2nd rowPremium
3rd rowFree
4th rowPremium
5th rowBasic

Common Values

ValueCountFrequency (%)
Free 341
34.1%
Premium 334
33.4%
Basic 302
30.2%
(Missing) 23
 
2.3%

Length

2025-01-26T13:27:50.403313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:50.487390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
free 341
34.9%
premium 334
34.2%
basic 302
30.9%

Most occurring characters

ValueCountFrequency (%)
e 1016
19.5%
r 675
13.0%
m 668
12.8%
i 636
12.2%
F 341
 
6.5%
P 334
 
6.4%
u 334
 
6.4%
B 302
 
5.8%
a 302
 
5.8%
s 302
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1016
19.5%
r 675
13.0%
m 668
12.8%
i 636
12.2%
F 341
 
6.5%
P 334
 
6.4%
u 334
 
6.4%
B 302
 
5.8%
a 302
 
5.8%
s 302
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1016
19.5%
r 675
13.0%
m 668
12.8%
i 636
12.2%
F 341
 
6.5%
P 334
 
6.4%
u 334
 
6.4%
B 302
 
5.8%
a 302
 
5.8%
s 302
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1016
19.5%
r 675
13.0%
m 668
12.8%
i 636
12.2%
F 341
 
6.5%
P 334
 
6.4%
u 334
 
6.4%
B 302
 
5.8%
a 302
 
5.8%
s 302
 
5.8%

Color of Watch
Categorical

Missing 

Distinct2
Distinct (%)0.2%
Missing54
Missing (%)5.4%
Memory size7.9 KiB
Black
691 
White
255 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4730
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowBlack
3rd rowWhite
4th rowBlack
5th rowBlack

Common Values

ValueCountFrequency (%)
Black 691
69.1%
White 255
 
25.5%
(Missing) 54
 
5.4%

Length

2025-01-26T13:27:50.589734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:50.657129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black 691
73.0%
white 255
 
27.0%

Most occurring characters

ValueCountFrequency (%)
B 691
14.6%
l 691
14.6%
a 691
14.6%
c 691
14.6%
k 691
14.6%
W 255
 
5.4%
h 255
 
5.4%
i 255
 
5.4%
t 255
 
5.4%
e 255
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 691
14.6%
l 691
14.6%
a 691
14.6%
c 691
14.6%
k 691
14.6%
W 255
 
5.4%
h 255
 
5.4%
i 255
 
5.4%
t 255
 
5.4%
e 255
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 691
14.6%
l 691
14.6%
a 691
14.6%
c 691
14.6%
k 691
14.6%
W 255
 
5.4%
h 255
 
5.4%
i 255
 
5.4%
t 255
 
5.4%
e 255
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 691
14.6%
l 691
14.6%
a 691
14.6%
c 691
14.6%
k 691
14.6%
W 255
 
5.4%
h 255
 
5.4%
i 255
 
5.4%
t 255
 
5.4%
e 255
 
5.4%

Synchronisation
Boolean

Missing 

Distinct2
Distinct (%)0.2%
Missing50
Missing (%)5.0%
Memory size2.1 KiB
True
840 
False
110 
(Missing)
 
50
ValueCountFrequency (%)
True 840
84.0%
False 110
 
11.0%
(Missing) 50
 
5.0%
2025-01-26T13:27:50.711304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

User of latest model
Categorical

Missing 

Distinct2
Distinct (%)0.2%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
0.0
573 
1.0
405 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2934
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 573
57.3%
1.0 405
40.5%
(Missing) 22
 
2.2%

Length

2025-01-26T13:27:50.805872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T13:27:50.878513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 573
58.6%
1.0 405
41.4%

Most occurring characters

ValueCountFrequency (%)
0 1551
52.9%
. 978
33.3%
1 405
 
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1551
52.9%
. 978
33.3%
1 405
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1551
52.9%
. 978
33.3%
1 405
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1551
52.9%
. 978
33.3%
1 405
 
13.8%

Interactions

2025-01-26T13:27:41.363096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.073893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.474480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.942938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.052507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.493517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:37.251437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.682760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.049218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.507640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.220258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.626656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:31.098906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.206367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.635307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:37.459895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.837349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.192950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.642785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.360102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.848947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:31.830279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.347583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.786100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:37.616524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.984420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.343985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.794589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.512779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.998314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:32.740955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.536741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.935171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:37.779997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.127444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.489248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.958556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.648103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.154406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:33.004836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.701413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:36.084193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:37.940715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.275216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.641688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:42.104548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:28.781902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.312498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:33.261190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:34.855815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:36.215883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.128385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.423766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.784073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:42.227192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.053711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.456262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:33.505049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.018937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:36.348898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.258324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.571421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:40.925137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:42.369527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.206015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.621585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:33.702705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.180286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:36.537553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.403152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.744214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.080752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:42.513780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:29.345264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:30.778730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:33.880050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:35.344394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:36.690129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:38.554798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:39.899932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-26T13:27:41.223889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-26T13:27:50.980322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
10k Running Time PredictionAge of customerBiking Hours per WeekCalories Burned per WeekColor of WatchCtryCustomer YearsGoal of TrainingPreferred Training DaytimeRunning Hours per WeekSexSubscription TypeSupport Cases of CustomerSwimming Hours per WeekSynchronisationTotal Training Hours per WeekTownUser of latest modelVO2 Max
10k Running Time Prediction1.0000.743-0.165-0.3360.0520.044-0.0160.2020.051-0.4130.0530.0150.062-0.1960.077-0.3620.0570.378-0.843
Age of customer0.7431.0000.006-0.0110.0000.0330.1510.0250.073-0.0130.0000.0620.057-0.0660.046-0.0230.0440.390-0.749
Biking Hours per Week-0.1650.0061.0000.7940.0000.0000.0080.3450.0180.1340.0380.0000.0000.1110.0000.8030.0190.2370.271
Calories Burned per Week-0.336-0.0110.7941.0000.0200.0000.0350.3980.0560.5490.0740.0220.0560.4380.0460.9660.0670.2810.334
Color of Watch0.0520.0000.0000.0201.0000.0680.0000.0360.0160.0900.0000.0000.0000.0000.0000.0000.0120.0000.000
Ctry0.0440.0330.0000.0000.0681.0000.0000.0310.0280.0000.0000.0000.0000.0590.0640.0410.8120.3070.040
Customer Years-0.0160.1510.0080.0350.0000.0001.0000.0430.0000.0310.0000.0000.0400.0470.0000.0290.0000.1710.015
Goal of Training0.2020.0250.3450.3980.0360.0310.0431.0000.0000.2940.0310.0000.0000.2630.0000.3520.0340.2330.154
Preferred Training Daytime0.0510.0730.0180.0560.0160.0280.0000.0001.0000.0000.0000.0680.0000.0500.0670.0580.1000.0460.036
Running Hours per Week-0.413-0.0130.1340.5490.0900.0000.0310.2940.0001.0000.0460.0000.0440.1200.0000.5680.0000.1840.197
Sex0.0530.0000.0380.0740.0000.0000.0000.0310.0000.0461.0000.0300.0230.0000.0000.0000.0240.0200.000
Subscription Type0.0150.0620.0000.0220.0000.0000.0000.0000.0680.0000.0301.0000.0000.0000.0450.0000.0000.0930.000
Support Cases of Customer0.0620.0570.0000.0560.0000.0000.0400.0000.0000.0440.0230.0001.0000.0220.0000.0000.0320.1150.054
Swimming Hours per Week-0.196-0.0660.1110.4380.0000.0590.0470.2630.0500.1200.0000.0000.0221.0000.0000.4460.0390.1920.232
Synchronisation0.0770.0460.0000.0460.0000.0640.0000.0000.0670.0000.0000.0450.0000.0001.0000.0820.0000.0490.050
Total Training Hours per Week-0.362-0.0230.8030.9660.0000.0410.0290.3520.0580.5680.0000.0000.0000.4460.0821.0000.0680.2770.357
Town0.0570.0440.0190.0670.0120.8120.0000.0340.1000.0000.0240.0000.0320.0390.0000.0681.0000.3040.072
User of latest model0.3780.3900.2370.2810.0000.3070.1710.2330.0460.1840.0200.0930.1150.1920.0490.2770.3041.0000.378
VO2 Max-0.843-0.7490.2710.3340.0000.0400.0150.1540.0360.1970.0000.0000.0540.2320.0500.3570.0720.3781.000

Missing values

2025-01-26T13:27:42.784924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-26T13:27:43.186448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-26T13:27:43.587659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDAge of customerSexCtryTownSwimming Hours per WeekBiking Hours per WeekRunning Hours per WeekTotal Training Hours per WeekVO2 Max10k Running Time PredictionCalories Burned per WeekSupport Cases of CustomerCustomer YearsMost current software updateGoal of TrainingPreferred Training DaytimeSubscription TypeColor of WatchSynchronisationUser of latest model
0C000161.0OtherUKBirmingham2.520.101.784.4023.080060.722329.952.01.02024-07-22FitnessEveningFreeWhiteYes1.0
1C000357.0OtherAustraliaSydney1.5510.014.5716.1339.040054.377904.932.01.02024-05-11FitnessEveningPremiumBlackYes0.0
2C000430.0OtherIndiaBangalore1.1912.048.6421.8771.590033.9210839.812.00.02024-08-16CompetitionAfternoonFreeWhiteYes1.0
3C000521.0MaleGermanyMunich2.254.670.927.8449.090044.97NaN3.00.02024-08-11RecreationEveningPremiumBlackNo1.0
4C000663.0MaleIndiaMumbai0.805.880.677.3513.910062.463575.963.03.02024-11-18RecreationMorningBasicBlackYes0.0
5C000731.0OtherIndiaMumbai1.016.740.568.3062.330040.674127.602.07.02024-08-22RecreationMorningBasicBlackYes0.0
6C000830.0FemaleUKNaN0.844.064.239.12247.257836.564835.714.03.02024-02-03RecreationAfternoonFreeBlackYes0.0
7C000944.0OtherIndiaBangalore1.956.392.1910.5447.740045.325156.953.02.02024-11-11RecreationAfternoonBasicBlackYes0.0
8C001023.0OtherUSANew York0.003.043.896.9359.190038.403705.742.01.02024-07-04RecreationEveningFreeBlackYes0.0
9C001134.0OtherUKBirmingham0.936.712.079.7159.880043.364692.400.03.02024-04-22RecreationAfternoonFreeWhiteYes0.0
IDAge of customerSexCtryTownSwimming Hours per WeekBiking Hours per WeekRunning Hours per WeekTotal Training Hours per WeekVO2 Max10k Running Time PredictionCalories Burned per WeekSupport Cases of CustomerCustomer YearsMost current software updateGoal of TrainingPreferred Training DaytimeSubscription TypeColor of WatchSynchronisationUser of latest model
990C099128.0FemaleUSAChicago1.663.523.598.7854.6438.944570.564.07.02023-12-28FitnessEveningFreeBlackYes0.0
991C099245.0OtherIndiaBangalore3.1810.771.8615.8156.3941.588139.921.00.02024-11-03FitnessMorningFreeBlackNo0.0
992C099319.0FemaleUSANew York3.515.340.008.8558.0747.694704.271.01.02024-11-02RecreationAfternoonBasicBlackNo1.0
993C099425.0OtherUSANew York2.594.001.157.7458.3144.484001.434.03.02024-09-10CompetitionAfternoonBasicWhiteYes1.0
994C099546.0FemaleUKLondon2.771.292.066.1249.0544.213088.671.06.02023-11-18RecreationEveningBasicWhiteYes0.0
995C099628.0FemaleAustraliaBrisbane2.380.282.795.4661.0540.382589.770.03.02024-01-09RecreationMorningPremiumBlackYes0.0
996C099754.0FemaleAustraliaMelbourne1.804.451.357.6134.5056.384104.150.03.02024-11-09RecreationAfternoonFreeBlackYes0.0
997C099835.0OtherUSANew York3.910.575.5710.0558.6337.275078.454.06.02024-08-27FitnessAfternoonPremiumBlackYes1.0
998C099931.0OtherGermaynBerlin0.364.635.1610.1566.4637.985184.273.03.02024-06-19FitnessEveningFreeBlackNaN1.0
999C100027.0MaleIndiaMumbai0.437.465.5513.4461.8136.076827.690.07.02024-06-04FitnessAfternoonPremiumBlackYes0.0

Duplicate rows

Most frequently occurring

IDAge of customerSexCtryTownSwimming Hours per WeekBiking Hours per WeekRunning Hours per WeekTotal Training Hours per WeekVO2 Max10k Running Time PredictionCalories Burned per WeekSupport Cases of CustomerCustomer YearsMost current software updateGoal of TrainingPreferred Training DaytimeSubscription TypeColor of WatchSynchronisationUser of latest model# duplicates
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN21
0C064029.0OtherAustraliaMelbourne5.061.440.256.7554.8643.603233.520.03.02024-11-22FitnessEveningBasicBlackYes0.02
1C070855.0OtherUKBirmingham0.405.892.078.3740.7351.154460.390.07.02024-10-09RecreationMorningPremiumWhiteNo0.02